Dynamic NeRFs reconstruct moving scenes from multi-view inputs or given pre-defined deformation template [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}.
In contrast, there is a series of recent works that seek to synthesize high-quality novel views of generic dynamic scenes given a monocular video [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}.
These works can be classified into two categories: a deformed scene is directly modeled as a time-varying NeRF in the world space [9]}, [12]}, [14]}
or as a NeRF in canonical space with a time-dependent deformation [10]}, [11]}, [13]}, [15]}, [16]}.
The evaluation protocol in these works inherit from the original static-scene NeRF [25]} that quantify the rendering quality of held-out viewpoints using image metrics, e.g., PSNR.
However, in dynamic scenes, PSNR from an unseen camera view may not be meaningful since the novel view may include regions that were never seen in the training view (unless the method can infer unseen regions using learning based approaches).
Existing approaches resolve this issue by incorporating views from multiple cameras during training, which we show results in an effectively multi-view setup.
We introduce metrics to measure the difficulties of an input sequence, a monocular dataset with new evaluation protocol and metrics, which show that existing methods have a large room for improvement.
